Template-Type: ReDIF-Paper 1.0
Author-Name: Peiris, S.
Author-Name-Last: Peiris
Author-Name-First: Shelton
Author-Name: Asai, M.
Author-Name-Last: Asai
Author-Name-First: Manabu
Author-Person: pas73
Author-Name: McAleer, M.J.
Author-Name-Last: McAleer
Author-Name-First: Michael
Author-Person: pmc90
Title: Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
Abstract: In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.
Length: 24
Creation-Date: 2016-06-01
File-URL: https://repub.eur.nl/pub/93114/EI2016-27.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI2016-27
Classification-JEL: C18, C21, C58
Keywords: Stochastic volatility, GARCH models, Gegenbauer Polynomial, Long Memory, Spectral Likelihood, Estimation, Forecasting
Handle: RePEc:ems:eureir:93114